A novel heuristic method for the energy-efficient flexible job-shop scheduling problem with sequence-dependent set-up and transportation time

With the increasing attention on environmental issues, green scheduling in manufacturing industries has become a hot research topic. As a typical scheduling problem, the flexible job-shop scheduling problem (FJSP) has received increasing attention, but research on the FJSP considering set-up and transportation times simultaneously is still rare. To address the energy-efficient FJSP with sequence-dependent set-up and transportation times to minimize makespan and total energy consumption, a multi-objective mixed-integer linear programming model of the problem is formulated and an effective novel heuristic method (NHM) is proposed. To enhance the convergence and distribution of the NHM, three strategies-population initialization, greedy iterative decoding and local intensification-are designed. The performance of the NHM is demonstrated by comparison with three algorithms through 48 instances. The results show that the NHM can obtain a scheduling scheme with lower makespan and total energy consumption than those of the comparison algorithms.

Automated summary

This study proposes a multi-objective mixed-integer linear programming model to solve the energy-efficient FJSP considering sequence-dependent set-up and transportation times, and introduces an effective novel heuristic method. Through comparison with 48 instances, the method is demonstrated to outperform other algorithms, obtaining scheduling schemes with lower makespan and total energy consumption.